Senior Staff Engineer, PCB Design (R5752)
New
You'll work on VLSI physical design flow enhancement, including floorplanning, power distribution, placement, routing, and verification, plus in-house tool development and AI workflow optimization. The role requires pursuit of an MS or higher in EE, CS, or Mathematics/Physics, with knowledge of IC design synthesis, timing, floorplanning, and congestion analysis. Strong programming fundamentals, learning capability, and debugging skills are essential. Hands-on IC design experience, Python or Tcl proficiency, and ML project background are advantageous.
Written from this posting by Neural Jobs AI. The full description is below.
We are now looking for VLSI Physical Design Interns!
VLSI Physical Design Team at NVIDIA China has been built up since 2005. The team has made contribution to various successful products launched by NVIDIA Corporation over 30 years. We utilize latest process technology, advanced EDA tools, and sophisticated design methodology. We always work on the most challenging designs, and push for performance limit.
What you will be doing:
Work on flow enhancement of in-house flow on floorplan, power/clock distribution, placement, routing, timing/power/noise analysis, chip assembly, and physical verification
Work on in-house tool development and exploration
Look for AI solution to improve workflow efficiency and automation
What we need to see:
Pursuing MS (or higher) in EE, CS and Mathematics/Physics
Knowledge of whole flow of IC design, especially synthesis/timing/floorplan/congestion/drc analysis
Fundamentals of algorithm and programming skills
Fast capability of learning and strong debugging skills shown up in past hands-on project
Ways to stand out from the crowd:
Hands-on background in IC design or physical design
Hands-on experience in popular programming language (Python/tcl etc.)
DL/ML project experience
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NVIDIA builds the GPUs and the CUDA software stack that most modern AI is trained and served on, along with its own research in graphics, robotics and foundation models.
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